Features

Features

AI infrastructure

• AI hardware vendors band together to challenge Nvidia

  An industry group including Arm and Intel seeks to increase the number of options in the AI market and decrease developers' dependence on GPUs.  Continue Reading

• The need for common sense in AI systems

  Building explainable and trustworthy AI systems is paramount. To get there, computer scientists Ron Brachman and Hector Levesque suggest infusing common sense into AI development.  Continue Reading

• AI, the 2024 U.S. election and the spread of disinformation

  Generative technology-fueled deepfakes could interfere with the November election due to ease of use and power of the technology. The outlook for regulation seems dim.  Continue Reading

• A guide to artificial intelligence in the enterprise

  AI in the enterprise is changing how work is done, but companies must overcome various challenges to derive value from this powerful and rapidly evolving technology.  Continue Reading

• 10 top AI and machine learning trends for 2024

  Custom enterprise models, open source AI, multimodal -- learn about the top AI and machine learning trends for 2024 and how they promise to transform the industry.  Continue Reading

• Compare GPUs vs. CPUs for AI workloads

  GPUs are often presented as the vehicle of choice to run AI workloads, but the push is on to expand the number and types of algorithms that can run efficiently on CPUs.  Continue Reading

• Big money investments, not acquisitions, fuel GenAI startups

  With the generative AI explosion comes a new trend for the tech giants. Instead of buying smaller companies, big cloud vendors are partnering with the startups.  Continue Reading

• Generative AI as a copilot for finance and other sectors

  While many fear that the popularity of large language models could lead to job loss and replacement, some industries such as finance and education are using AI to augment workers.  Continue Reading

• The future of generative AI: How will it impact the enterprise?

  Learn how generative AI will affect organizations in terms of capabilities, enterprise workflows and ethics, and how the technology will shape enterprise use cases.  Continue Reading

• Designing systems that reduce the environmental impact of AI

  Understanding AI's full climate impact means looking past model training to real-world usage, but developers can take tangible steps to improve efficiency and monitor emissions.  Continue Reading

• What is regression in machine learning?

  Regression in machine learning helps organizations forecast and make better decisions by revealing the relationships between variables. Learn how it's applied across industries.  Continue Reading

• Attributes of open vs. closed AI explained

  What's the difference between open vs. closed AI, and why are these approaches sparking heated debate? Here's a look at their respective benefits and limitations.  Continue Reading

• A look at open source AI models

  Open source AI models have advantages over generative AI services offered by major cloud providers. But enterprises have to weigh the benefits against the costs.  Continue Reading

• IT observability tool proliferation fuels AIOps deployments

  Enterprise Strategy Group's Jon Brown discusses the latest findings in his newly released report on observability in IT and application infrastructures and integrating AIOps.  Continue Reading

• AI existential risk: Is AI a threat to humanity?

  What should enterprises make of the recent warnings about AI's threat to humanity? AI experts and ethicists offer opinions and practical advice for managing AI risk.  Continue Reading

• CNN vs. GAN: How are they different?

  Convolutional neural networks and generative adversarial networks are both deep learning models but differ in how they work and are used. Learn the ins and outs of CNNs and GANs.  Continue Reading

• How AI can assist industries in environmental protection efforts

  While technology for environmental protection isn't a new concept, AI advancements empower businesses to achieve sustainable operations.  Continue Reading

• How AI in weather prediction can aid human intelligence

  AI and machine learning models are becoming more widely used in climate prediction and disaster preparedness to aid experts without replacing them.  Continue Reading

• James Earl Jones, AI and the growing voice cloning market

  The growing text-to-speech and speech-to-speech market is sparking new enterprise applications. However, the technologies also raise concerns about privacy and misuse of the tools.  Continue Reading

• Defining requirements key to manage machine learning projects

  Machine learning projects are likely to fail without proper planning. 'Managing Machine Learning Projects' provides guidance on how to plan by defining ML project requirements.  Continue Reading

• How AI and automation play a role in ITOps

  Tech professionals agree that AI, intelligent automation and cybersecurity play important roles in the enterprise and can revolutionize ITOps when implemented and used correctly.  Continue Reading

• Spotify personalizes audio experiences with machine learning

  The streaming platform builds models using analytics, data from users and content to create a personalized audio experience for users and try to keep them as long-term customers.  Continue Reading

• AI and disinformation in the Russia-Ukraine war

  From false videos circulating on TikTok to AI-generated humans and deepfakes, the Russia-Ukraine war is playing out both in the physical world and virtually.  Continue Reading

• Swiss retailer uses open source Ray tool to scale AI models

  Ricardo uses Anyscale's Ray for scaling its product classification models. Ray helps enterprises scale their applications from a laptop to the cloud.  Continue Reading

• Piloting machine learning projects through harsh headwinds

  To get machine learning projects off the ground and speed deployments, data science teams need to ask questions on a host of issues ranging from data quality to product selection.  Continue Reading

• How cloud RPA is key to automation's future

  Companies have traditionally used robotic process automation (RPA) as on-premises software but are now embracing cloud RPA as its business benefits are outweighing the drawbacks.  Continue Reading

• Efforts to craft AI regulations will continue in 2022

  Regulating AI can be challenging for many reasons, including varying definitions of fairness and explainability. However, AI regulations will be a top focus for lawmakers in 2022.  Continue Reading

• Language models and the metaverse top AI stories of 2021

  From moves toward government regulation to the metaverse, language models getting bigger and autonomous vehicle tech slowing, these are some of the biggest stories of the year.  Continue Reading

• SambaNova makes a mark in the AI hardware realm

  The startup says it is innovating AI hardware systems with its data flow architecture that enterprises can use to be more efficient when processing large AI data sets.  Continue Reading

• A look at AI trends and bias in AI algorithms

  In the past few years, more and more organizations have focused on AI. However, just as the use of AI and machine learning has expanded, concern about AI bias is also growing.  Continue Reading

• Expanding explainable AI examples key for the industry

  Improving AI explainability and interpretability are keys to building consumer trust and furthering the technology's success.  Continue Reading

• Why transparency in AI matters for businesses

  To ensure model accuracy, businesses need to understand why their machine learning models make their decisions. Certain tools and techniques can help with that.  Continue Reading

• Advanced SQL skills boost data scientists' value

  Learning advanced SQL skills can help data scientists effectively query their databases and unlock new insights into data relationships, resulting in more useful information.  Continue Reading

• Designing and building artificial intelligence infrastructure

  Building an artificial intelligence infrastructure requires a serious look at storage, networking and AI data needs, combined with deliberate and strategic planning.  Continue Reading

• 8 considerations for buying versus building AI

  Business leaders should consider their employees' technical expertise, technology budgets and regulatory needs, among other factors, when deciding to build or buy AI.  Continue Reading

• Addressing 3 infrastructure issues that challenge AI adoption

  One of the biggest problems enterprises run into when adopting AI infrastructure is using a development lifecycle that doesn't work when building and deploying AI models.  Continue Reading

• Edge AI brings new uses to IoT devices

  A Lenovo executive describes AI at the edge, highlighting how this rapidly advancing technology unlocks new automations and capabilities within IoT devices.  Continue Reading

• Nvidia acquisition of Arm faces industry, regulatory hurdles

  Nvidia's acquisition of Arm Ltd. could change the chipmaker landscape and is reportedly raising industry and regulatory eyebrows.  Continue Reading

• Artificial general intelligence in business holds promise

  While AGI in business remains unattainable today, truly intelligent systems, chatbots and predictive analytics are potential use cases enterprises should keep their eyes on.  Continue Reading

• Data democratization strategy for machine learning enterprise

  In the enterprise, data democratization works to break down data silos by opening access to an organization's data across teams in an effort to improve workflows.  Continue Reading

• The power and limitations of enterprise AI

  A panel at CES 2021, held virtually this year, discusses the areas in which modern-day AI and automation shine, and where they still struggle.  Continue Reading

• Defining enterprise AI: From ETL to modern AI infrastructure

  The promise of enterprise AI is built on old ETL technologies, and it relies on an AI infrastructure effectively integrating and processing loads of data.  Continue Reading

• KDD in data mining assists data prep for machine learning

  While data scientists are often familiar with data mining, the deeper knowledge discovery in databases (KDD) procedure can help prepare data to train machine learning algorithms.  Continue Reading

• Finding the balance between edge AI vs. cloud AI

  Centralized cloud resources allow AI to continuously improve while edge AI allows for real-time decision-making and larger models. The best approach combines them.  Continue Reading

• Emerging AI startups to look at in 2021

  AI startups in the legal, MLOps, NLP and data training markets make this year's list of emerging AI vendors to look out for.  Continue Reading

• Insurance provider uses AI for legal to manage contracts

  The legal team at Asurion, a major insurance provider, kept its documents spread out across filing cabinets, hard drives and the cloud. It turned to an AI platform to better manage them.  Continue Reading

• Understanding motion analytics, where it is and where it's going

  Machine learning is helping make motion analysis more usable for the average enterprise, creating new use cases and applications that can drive value.  Continue Reading

• Machine learning limitations marked by data demands

  Machine learning has impressive capabilities in the enterprise, but with high-data requirements and struggles with explainability, it remains unable to reach widespread use.  Continue Reading

• Enterprises should not neglect AI digital transformation

  Enterprises should focus on automation to augment their workforces as they recover from the COVID-19 economic downturn, and not lose sight of larger digital transformation projects.  Continue Reading

• Cloud computing for machine learning offers on-demand tools

  Automated machine learning and MLaaS tools are now being developed for the cloud, and enterprises need better workflows and infrastructure to successfully integrate the technology.  Continue Reading

• Are giant AI chips the future of AI hardware?

  Giant AI chips like the Cerebras WSE are dazzlingly fast and could transform AI models, but how soon is the question for CIOs. Experts mull the merits of small vs. big AI chips.  Continue Reading

• Using ModelOps, a financial services company scales out

  Using model operations (ModelOps), a fintech startup was able to scale up its model deployment quickly, while also maintaining model governance at scale.  Continue Reading

• Common sense in AI remains elusive

  While AI and machine learning have made major improvements and advancements to computers, common sense in AI has proven to be a significant challenge.  Continue Reading

• Data center energy usage combated by AI efficiency

  Though often forgotten by the general public, data centers account for 1% of the world's energy consumption. Explore what brought about data centers and how AI can be used to help mitigate their presence.  Continue Reading

• The state of AI defined by global adoption and regulation

  Cognilytica reports on AI adoption by both countries and companies across the globe, as well as the former's overall strategies and regulation frameworks.  Continue Reading

• Merging DevOps and machine learning requires restructuring

  Companies that are restructuring in order to merge their traditional DevOps teams with their machine learning efforts to aid with accessibility need to include voices from multiple teams.  Continue Reading

• Standards for data sharing should guide AI government regulation

  The White House has taken a deregulatory approach to AI and aims to inspire innovation. An expert weighs in on the role of government in AI and where the industry stands.  Continue Reading

• Patience is pivotal for the autonomous vehicle future

  The fatal collision between an Uber ATG vehicle and a pedestrian was a reminder that autonomous vehicles are not ready and that a difficult technological hill remains.  Continue Reading

• The rising use of AI in the energy sector

  The energy industry has begun to ramp up the adoption of AI with the usage of smart grids among other technologies and has seen the benefits of the technology.  Continue Reading

• How to overcome 4 major challenges in AI adoption

  While companies are stuck in the research phase of AI, a few simple infrastructure analyzations can jumpstart the process -- and ensure successful deployment.  Continue Reading

• The complex nature of regulating AI

  Regulating AI is a difficult task because the technology changes rapidly. Governments must be able to employ preventative regulation to prevent any misuse.  Continue Reading

• AI vendors to watch in 2020 and beyond

  The past 10 years have seen a surge of new AI vendors, and the trend isn't likely to end anytime soon, as investors continue to pour money into artificial intelligence.  Continue Reading

• AI trends 2020 outlook: More automation, explainability

  Top AI trends for 2020 are increased automation to extend traditional RPA, deeper explainable AI with more natural language capacity, and better chips for AI on the edge.  Continue Reading

• How GE uses a 'Humble AI' approach to manufacturing

  GE executive Colin Parris explains why a deliberate approach to deploying AI is needed when dealing with products that cost hundreds of millions of dollars to make.  Continue Reading

• AI in networking helps keep systems running

  Network administrators are increasingly using AI tools to help them manage the growing complexity of their network infrastructure, a task that's getting more complicated by the day.  Continue Reading

• Serverless machine learning reduces development burdens

  Getting started with machine learning throws multiple hurdles at enterprises. But the serverless computing trend, when applied to machine learning, can help remove some barriers.  Continue Reading

• Choosing the right chip foundation for AI-optimized hardware

  Every enterprise is trying to implement AI and machine learning. But, before AI, before clean data and before platform comparison, enterprises need to find the best hardware to support AI.  Continue Reading

• Key considerations for operationalizing machine learning

  Once a machine learning model is trained, developers need to operationalize it. This turns out to be a significant challenge for many enterprises.  Continue Reading

• Reinforcement learning and deep learning pairing pushes AI limits

  The pairing of reinforcement and deep learning is enabling researchers to push the boundaries of what AI can do and could help contribute to advanced applications.  Continue Reading

• How AI in physical security makes public places safer

  Deep learning-based tools are increasingly finding a home in physical security to enhance the protection of real-world assets and make public spaces safer.  Continue Reading

• GPU analytics speeds up deep learning, other data insights

  GPU-based systems have become a popular platform for deep learning applications, and they're now also being used to accelerate analysis of IoT and geospatial data.  Continue Reading

• The future of voice assistants is multiturn conversations

  The future looks promising for voice assistants, but for them to really live up to the hype, they are going to have to improve at true multiturn conversations.  Continue Reading

• Berklee uses SnapLogic for its AI in higher education needs

  Berklee College of Music needed intelligent integration for its two student portals after a merger with the Boston Conservatory. The college chose SnapLogic to connect the systems.  Continue Reading

• ShopRunner uses Databricks for machine learning in retail

  Databricks user ShopRunner talks about the tools showed at Spark + AI Summit 2019, such as MLflow and Databricks Delta Lake. And Datameer reveals new Databricks integration.  Continue Reading

• Federated deep learning offers new approach to model training

  Training deep learning models puts a massive strain on enterprise infrastructure, but federated learning, which trains models on endpoint devices, could lessen some of the demand.  Continue Reading

• Data preparation for machine learning still requires humans

  Looking to AI to automate more of your processes? Don't overlook the labor that's still needed to prepare data for training machine learning and AI algorithms.  Continue Reading

• Researchers race for quantum AI as quantum computing advances

  Machine learning is likely to be an early application of quantum computers, as researchers and developers look for the key to a more human-like artificial intelligence.  Continue Reading

• AI in accounting boosts compliance and fraud detection

  Accounting and finance teams are using AI tools to speed document review and other error-prone processes, which gives a boost to fraud detection and compliance efforts.  Continue Reading

• AI as a service democratizes benefits of new tech tools

  The emergence of AI-as-a-service tools is helping more enterprises access the benefits of AI, not just the leading-edge tech companies that pioneered the technology.  Continue Reading

• AI for customer experience powers gains at enterprises

  Customer experience is growing more central to enterprises' digital strategies, and AI is increasingly driving much of their engagement and retention efforts.  Continue Reading

• AI use in healthcare ramps up for app maker Cognoa

  Applications of AI in healthcare have been relatively restricted due to regulatory and data challenges, but one startup is finding ways to make AI effective.  Continue Reading

• Countries need national strategy for AI to stay competitive

  With the Chinese government funding artificial intelligence at an aggressive pace, the U.S. and other countries are facing substantial pressure to step up their investment.  Continue Reading

• AI cybersecurity tools help spot threats before they cause harm

  Security pros are increasingly using AI-based cybersecurity tools to stay one step ahead of hackers and minimize vulnerabilities before they can be exploited by bad actors.  Continue Reading

• Machine learning in production challenges developers' skills

  Deploying machine learning models requires an entirely different skill set than developing them, and data scientists and engineering teams need to be ready to bridge this gap.  Continue Reading

• GPU cloud tools take complexity out of machine learning infrastructure

  While talk of AI on GPUs is abuzz, actually building a machine learning infrastructure remains a dark art. A startup's PaaS is looking to automate parts of the process.  Continue Reading

• Tech experts weigh in on the AI hype cycle

  AI expectations couldn't be any higher. Read why leading industry experts believe the hype is deserved and what developers can do to deliver on the technology's weighty promise.  Continue Reading

• A look at the leading artificial intelligence infrastructure products

  The artificial intelligence infrastructure market is young and varied, with enterprise AI vendors offering everything from cloud services to powerful, and expensive, hardware.  Continue Reading

• IT, finance and marketing have uses for AI -- do you?

  AI in healthcare improves patient outcomes. AI in IT aids employee compliance and security. AI in logistics, AI in marketing, AI in finance -- learn how your company can use AI.  Continue Reading